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the NSF Grants.gov Application Guide; A Guide for the Preparation and Submission of NSF Applications
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(Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the
NSF website at: http://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide)

Important Information for Proposers

A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 15-1), is
effective for proposals submitted, or due, on or after December 26, 2014. The PAPPG is consistent
with, and, implements the new Uniform Administrative Requirements, Cost Principles, and Audit
Requirements for Federal Awards (Uniform Guidance) (2 CFR § 200). Please be advised that
the guidelines contained in NSF 15-1 apply to proposals submitted in response to this
funding opportunity.

DUE DATES

Full Proposal Deadline Date: August 13, 2015

August 13, Annually Thereafter

Full Proposal Deadline Date: February 11, 2016

February 11, Annually Thereafter

SYNOPSIS

I. INTRODUCTION

The National Science Foundation announces the area of Cognitive Neuroscience within the Division of Behavioral and Cognitive Sciences in the Directorate for Social, Behavioral, and Economic Sciences.

Cognitive neuroscience is an interdisciplinary field of research dedicated to the understanding of the neural mechanisms underlying human cognition. As this field continues to grow, the National Science Foundation intends for cognitive neuroscience emphases to spur the development of highly novel theories, techniques and models directed toward enabling basic scientific understanding of a broad range of issues involving brain, cognition, and behavior. The emphasis at NSF is on the integration of cognitive, social and economic science in service of insights into healthy functions of brain, cognition, and behavior. Additionally, NSF highly values the exploration of new methodologies, utilization of the latest analytic approaches, and the convergence of cutting edge techniques for addressing basic questions about human cognition.

New frontiers in cognitive neuroscience research have emerged from investigations that integrate data at different spatial and temporal scales from a variety of techniques. The scientific study of cognitive neuroscience includes neuroimaging techniques for measuring or inferring neural activity, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI); optical imaging techniques for measuring vascular changes, such as near infrared spectroscopy (NIRS); techniques for sampling large population-level activity with superb temporal resolution, such as electroencephalography (EEG) and magnetoencephalography (MEG), and electrocorticography (ECoG); and techniques for determining structure-function relationships, such as diffusion imaging techniques (tensor, weighted, spectral). Additional techniques include non-invasive brain stimulation methods, such as transcranial magnetic stimulation (TMS) and electrical stimulation (tES) techniques that may use direct current (tDCS), alternating current (tACS) or random noise (tRNS) modes of stimulation. Other techniques include brain lesion-symptom mapping, neurogenetic approaches and computational modeling. The data from such varied sources can be further clarified by comparison with invasive neurophysiological recordings in non-human primates and other mammals. Additional recent advances include machine-learning and multivariate analysis methods, resting-state and task-based connectomics and large-scale data analysis. Combinatorial techniques now allow for the simultaneous application of research methodologies, such as TMS, EEG and fMRI; other advances have led to model-based approaches, wherein computational cognitive models may directly inform neuroimaging results. With the advent of new techniques and combinations, current progress in the field of cognitive neuroscience has moved from a modular, region-of-interest (ROI), correlative approach, to a network-based description of neural states, with a focus on causal mechanisms and connectivity. The cognitive neuroscience program seeks to emphasize that although ROI approaches may still be necessary, such approaches will only be considered competitive if they provide an advance in understanding causal mechanisms.

Findings from cognitive neuroscience can elucidate functional brain organization, such as the operations performed by a particular brain area within a network of distributed, discrete neural areas supporting specific cognitive, perceptual, motor, or affective operations or representations. Moreover, these findings can reveal the effect on brain organization of individual differences (including genetic variation), plasticity, and recovery of function following damage to the nervous system. Cognitive neuroscience can also elucidate the duration and sequencing of sub-processes, for example, by integrating high temporal resolution MEG data with high spatial resolution fMRI within subject and task. Such finely calibrated data can then inform cognitive and behavioral process models. Finally, subsequent comparisons of brain organization across species may allow the neural basis of such processes to be understood in a biological context.

II. PROGRAM DESCRIPTION

The Cognitive Neuroscience program seeks highly innovative proposals aimed at advancing a rigorous understanding of human cognition, including how the human brain mediates action, affect, creativity, decision making, intentionality, perception, social processes, and thought. Topics may bear on core functions such as attention, emotion, empathy, executive processes, language, learning, memory, music, sensory processing, sleep, representation of self and other, reasoning and rhythm. Topics may also include how human cognition develops and changes in the brain across the lifespan.

The program is particularly interested in supporting the development of new techniques and technologies for recording, analyzing, and modeling complex brain activity and human brain mapping. Such projects should include a plan for sharing new software and other technologies with the research community at large. Additionally, the program is interested in supporting projects addressing the growing amount of data collected across disparate lab environments, which may require new standardization, curation, and sharing solutions.

Studies of disease states (e.g., Alzheimer’s disease, Autism, brain damaged patients, Parkinson’s disease and Schizophrenia) may be components of projects supported by this program. However, the emphasis in such projects must be to advance basic scientific understanding of healthy neural mechanisms, and not on disease etiology, diagnosis, or treatment.

The program also intends to foster projects that integrate perspectives across disciplines, e.g., from the cognitive sciences, psychology, developmental sciences, biology, computer science, engineering, education, anthropology, physics, mathematics and statistics. For example, projects that involve collaborations among individuals with expertise in one of the cognitive sciences, neuroimaging, neural microcircuitry, and modeling complex systems are strongly encouraged.

Examples of appropriate grant proposals include, but are not be limited to, the following. It is to be expected that scientific advances will overtake many of the following issues, and that other research and development matters will emerge as key enablers to progress in basic cognitive neuroscience.

Proposals related to the development of new, or integration of, existing methodologies to address cognitive questions involving human or non-human primates.

Application of computational techniques or models for addressing cognitive questions or issues of data analysis.

Proposals examining non-stationary effects across different time windows spanning several orders of magnitude, such as learning and developmental paradigms in young, aging, healthy or impaired groups.

Development and utilization of brain stimulation or symptom-mapping methods in conjunction with advanced behavioral analysis for determining causal linkages between neural networks and cognitive functions.